12 ms·

It feels like you're purposefully ignoring the logical points OP gives and you just really really want to anthropomorphize AlphaGo and make us appreciate how sm

by wobfan 6mo ago

It feels like you're purposefully ignoring the logical points OP gives and you just really really want to anthropomorphize AlphaGo and make us appreciate how smart it (should I say he/she?) is ... while no one is even criticising the model's capabilities, but analyzing it.

famouswaffles 6mo ago
[dead]
trick-or-treat 6mo ago
Can you back that up with some logic for me? I don't really play Go but I play chess, and it seems to me that most of what humans consider creativity in GM level play comes not in prep (studying opening lines/training) but in novel lines in real games (at inference time?). But that creativity absolutely comes from recalling patterns, which is exactly what OP criticizes as not creative(?!) I guess I'm just having trouble finding a way to move the goalpost away from artificial creativity that doesn't also move it away from human creativity?
- datsci_est_2015 6mo ago
  How a model is trained is different than how a model is constructed. A model’s construction defines its fundamental limitations, e.g. a linear regressor will never be able to provide meaningful inference on exponential data. Depending on how you train it, though, you can get such a model to provide acceptable results in some scenarios. Mixing the two (training and construction) is rhetorically convenient (anthropomorphization), but holds us back in critically assessing a model’s capabilities.
  - hackinthebochs 6mo ago
    Linear regression has well characterized mathematical properties. But we don't know the computational limits of stacked transformers. And so declaring what LLMs can't do is wildly premature.
    - datsci_est_2015 6mo ago
      > And so declaring what LLMs can't do is wildly premature. The opposite is true as well. Emergent complexity isn’t limitless. Just like early physicists tried to explain the emergent complexity of the universe through experimentation and theory, so should we try to explain the emergent complexity of LLMs through experimentation and theory. Specifically not pseudoscience, though.
      - hackinthebochs 6mo ago
        Sure, that's true as well. But I don't see this as a substantive response given that the only people making unsupported claims in this thread are those trying to deflate LLM capabilities.
        datsci_est_2015 6mo ago
        So, to review this thread - OP asked for someone to make a logical argument for the separation of “training” from “model” - I made the argument - You cherry picked an argument against my specific example and made an appeal to emergent complexity - I pointed out that emergent complexity isn’t limitless - “the only people making unsupported claims in this thread are those trying to deflate LLM capabilities”
        famouswaffles 6mo ago
        You made a pretty nonsensical argument, pretty much seems like the big standard for these arguments. What does linear regression have to do with the limitations of a stacked transfer ? Absolutely nothing. This is the problem here. You don't know shit and just make up whatever. You can see people doing the same thing in GPT-1, 2, 3, 4 threads all telling us why LLMs will never be able to do thing it manages to do later.
        datsci_est_2015 6mo ago
        > You don’t know shit lol. Why so emotionally charged? Are you perhaps worried that you’ve invested too much time and effort into a technology that may not deliver what influencers have been promising for years? Like a proverbial bagholder? > What does linear regression have to do with the limitations of a stacked transfer ? Absolutely nothing. This is the problem here. We’re talking about fundamental concepts of modeling in this subthread. LLMs, despite what influencers may tell you, are simply models. I’ll even throw you a bone and admit they are models for intelligence. But they are still models, and therefore all of the things that we have learned about “models” since Plato are still relevant. Most importantly, since Plato we’ve known that “models” have fundamental limits vs. what they try to represent, otherwise they would be a facsimile, not a model. > You can see people doing the same thing in GPT-1, 2, 3, 4 threads all telling us why LLMs will never be able to do thing it manages to do later. I hope you enjoy winning these imaginary arguments against these imaginary comments. The fundamental limitations of LLMs discussed since GPT-1 have never been addressed by changing the architecture of the underlying model. All of the improvements we’ve experienced have been due to (1) improvements in training regime and (2) harnesses / heuristics (e.g. Agents). Now, care to provide a counterargument that shows you know a little more than “shit”?

by wobfan 6mo ago

famouswaffles 6mo ago

[dead]

trick-or-treat 6mo ago

Can you back that up with some logic for me? I don't really play Go but I play chess, and it seems to me that most of what humans consider creativity in GM level play comes not in prep (studying opening lines/training) but in novel lines in real games (at inference time?). But that creativity absolutely comes from recalling patterns, which is exactly what OP criticizes as not creative(?!) I guess I'm just having trouble finding a way to move the goalpost away from artificial creativity that doesn't also move it away from human creativity?

datsci_est_2015 6mo ago
How a model is trained is different than how a model is constructed. A model’s construction defines its fundamental limitations, e.g. a linear regressor will never be able to provide meaningful inference on exponential data. Depending on how you train it, though, you can get such a model to provide acceptable results in some scenarios. Mixing the two (training and construction) is rhetorically convenient (anthropomorphization), but holds us back in critically assessing a model’s capabilities.
- hackinthebochs 6mo ago
  Linear regression has well characterized mathematical properties. But we don't know the computational limits of stacked transformers. And so declaring what LLMs can't do is wildly premature.
  - datsci_est_2015 6mo ago
    > And so declaring what LLMs can't do is wildly premature. The opposite is true as well. Emergent complexity isn’t limitless. Just like early physicists tried to explain the emergent complexity of the universe through experimentation and theory, so should we try to explain the emergent complexity of LLMs through experimentation and theory. Specifically not pseudoscience, though.
    - hackinthebochs 6mo ago
      Sure, that's true as well. But I don't see this as a substantive response given that the only people making unsupported claims in this thread are those trying to deflate LLM capabilities.
      - datsci_est_2015 6mo ago
        So, to review this thread - OP asked for someone to make a logical argument for the separation of “training” from “model” - I made the argument - You cherry picked an argument against my specific example and made an appeal to emergent complexity - I pointed out that emergent complexity isn’t limitless - “the only people making unsupported claims in this thread are those trying to deflate LLM capabilities”
        famouswaffles 6mo ago
        You made a pretty nonsensical argument, pretty much seems like the big standard for these arguments. What does linear regression have to do with the limitations of a stacked transfer ? Absolutely nothing. This is the problem here. You don't know shit and just make up whatever. You can see people doing the same thing in GPT-1, 2, 3, 4 threads all telling us why LLMs will never be able to do thing it manages to do later.
        datsci_est_2015 6mo ago
        > You don’t know shit lol. Why so emotionally charged? Are you perhaps worried that you’ve invested too much time and effort into a technology that may not deliver what influencers have been promising for years? Like a proverbial bagholder? > What does linear regression have to do with the limitations of a stacked transfer ? Absolutely nothing. This is the problem here. We’re talking about fundamental concepts of modeling in this subthread. LLMs, despite what influencers may tell you, are simply models. I’ll even throw you a bone and admit they are models for intelligence. But they are still models, and therefore all of the things that we have learned about “models” since Plato are still relevant. Most importantly, since Plato we’ve known that “models” have fundamental limits vs. what they try to represent, otherwise they would be a facsimile, not a model. > You can see people doing the same thing in GPT-1, 2, 3, 4 threads all telling us why LLMs will never be able to do thing it manages to do later. I hope you enjoy winning these imaginary arguments against these imaginary comments. The fundamental limitations of LLMs discussed since GPT-1 have never been addressed by changing the architecture of the underlying model. All of the improvements we’ve experienced have been due to (1) improvements in training regime and (2) harnesses / heuristics (e.g. Agents). Now, care to provide a counterargument that shows you know a little more than “shit”?